Abstract
Abstract Argumentation Frameworks (AFs) are used, in the field of Artificial Intelligence, to evaluate the justification state of conflicting information, thus allowing the development of automatic reasoning techniques and systems. Complex argumentative processes such as decision-making and negotiation, which take place over time, can be modelled through the Concurrent Language for Argumentation, a formalism for handling concurrent interactions between intelligent agents that use an AF as shared memory. In this paper, we first show how AFs can be interpreted as dependency graphs by exploiting the relation between arguments induced by the attacks. Then, we describe a methodology for obtaining a procedure that generates the given AF. Such a procedure allows to dynamically represent dialogues and other forms of interaction that brought to the instantiation of the specified AF.
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web interface: https://conarg.dmi.unipg.it/cla/. - 2.
An alternative option is to use maximum parallelism [6], for which processes composed through \(\Vert \) are executed at the same time. However, this approach may result in the numbering assigned to arguments by the feasible evaluation order not being unique.
web interface: References
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Stefano Bistarelli and Carlo Taticchi are members of the INdAM Research group GNCS and Consorzio CINI. This work has been partially supported by: INdAM - GNCS Project, CUP E53C22001930001; Project FICO, funded by Ricerca di Base 2021, University of Perugia; Project GIUSTIZIA AGILE, CUP J89J22000900005; Project BLOCKCHAIN4FOODCHAIN, funded by Ricerca di Base 2020, University of Perugia; Project VITALITY, CUP J97G22000170005, funded by NRRP-MUR.
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Bistarelli, S., Taticchi, C. (2023). Deriving Dependency Graphs from Abstract Argumentation Frameworks. In: Basili, R., Lembo, D., Limongelli, C., Orlandini, A. (eds) AIxIA 2023 – Advances in Artificial Intelligence. AIxIA 2023. Lecture Notes in Computer Science(), vol 14318. Springer, Cham. https://doi.org/10.1007/978-3-031-47546-7_2
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